Leveraging Conditional Generative Adversarial Networks (cGANs) for enhanced artistic creation: Exploring quality improvement and content control through conditional inputs

Abstract

Conditional Generative Adversarial Networks (cGANs) have revolutionized digital art by enabling the creation of high-quality, artistically coherent images guided by conditional inputs. This paper examines key factors influencing the performance of cGANs, including the quality of training data, network architecture improvements, and loss function optimization. We introduce a mathematical model to quantify training data quality, emphasizing dataset diversity, data augmentation, and cleaning. Network architectural enhancements such as residual connections, attention mechanisms, and progressive growing are explored for their impact on image quality. Additionally, we discuss the integration of conditional inputs, such as labels and textual descriptions, for precise content control. Challenges in balancing realism with artistic expression, managing mode collapse, and interpreting conditional inputs are also addressed. This study provides a comprehensive framework for enhancing cGAN-generated artworks, offering insights into applications in personalized art generation, art restoration, and collaborative art projects.